Genetic Algorithms for the Optimization of Catalysts in Chemical Engineering

The paper addresses key problems pertaining to the commonly used evolutionary approach to the search for optimal catalysts in chemical engineering. These are on the one hand the insufficient dealing in existing implementations of genetic algorithms with mixed optimization, which plays a crucial role in catalysis, on the other hand the narrow scope of genetic algorithms developed specifically for searching optimal catalyst. The paper proposes an approach to constrained mixed optimization based on formulating a separate linearly-constrained continuous optimization task for each combination of values of the discrete variables. Then, discrete optimization on the set of nonempty polyhedra describing the feasible solutions of those tasks is performed, followed by solving those tasks for each individual of the resulting population of polyhedra. To avoid computationally expensive checking of the non-emptiness of individual polyhedra, the set of polyhedra is first partitioned into equivalence classes such that only one representative from each class needs to be checked. Finally, the paper outlines a program generator automatically generating problem-tailored genetic algorithms from descriptions of optimization tasks in a specific description language, which employs the proposed approach to constrained mixed optimization.